1. CPU与GPU不同
1.输入数据
2.网络模型
3.损失函数
.cuda()
- 说明：下面代码中GPU版本中取消下划线的即为CPU版本
2.完成的分类代码(GPU)
import torch
import torchvision
from torch.utils.tensorboard import SummaryWriter# from model import *
# 准备数据集
from torch import nn
from torch.utils.data import DataLoader# 定义训练的设备
~~device = torch.device("cuda")~~ train_data = torchvision.datasets.CIFAR10(root="../data", train=True, transform=torchvision.transforms.ToTensor(),download=True)
test_data = torchvision.datasets.CIFAR10(root="../data", train=False, transform=torchvision.transforms.ToTensor(),download=True)# length 长度
train_data_size = len(train_data)
test_data_size = len(test_data)
# 如果train_data_size=10, 训练数据集的长度为：10
print("训练数据集的长度为：{}".format(train_data_size))
print("测试数据集的长度为：{}".format(test_data_size))# 利用 DataLoader 来加载数据集
train_dataloader = DataLoader(train_data, batch_size=64)
test_dataloader = DataLoader(test_data, batch_size=64)# 创建网络模型
class Tudui(nn.Module):def __init__(self):super(Tudui, self).__init__()self.model = nn.Sequential(nn.Conv2d(3, 32, 5, 1, 2),nn.MaxPool2d(2),nn.Conv2d(32, 32, 5, 1, 2),nn.MaxPool2d(2),nn.Conv2d(32, 64, 5, 1, 2),nn.MaxPool2d(2),nn.Flatten(),nn.Linear(64*4*4, 64),nn.Linear(64, 10))def forward(self, x):x = self.model(x)return x
tudui = Tudui()
~~tudui = tudui.to(device)~~ # 损失函数
loss_fn = nn.CrossEntropyLoss()
~~loss_fn = loss_fn.to(device)~~
# 优化器
# learning_rate = 0.01
# 1e-2=1 x (10)^(-2) = 1 /100 = 0.01
learning_rate = 1e-2
optimizer = torch.optim.SGD(tudui.parameters(), lr=learning_rate)# 设置训练网络的一些参数
# 记录训练的次数
total_train_step = 0
# 记录测试的次数
total_test_step = 0
# 训练的轮数
epoch = 10# 添加tensorboard
writer = SummaryWriter("../logs_train")for i in range(epoch):print("-------第 {} 轮训练开始-------".format(i+1))# 训练步骤开始tudui.train()for data in train_dataloader:imgs, targets = data~~imgs = imgs.to(device)targets = targets.to(device)~~ outputs = tudui(imgs)loss = loss_fn(outputs, targets)# 优化器优化模型optimizer.zero_grad()loss.backward()optimizer.step()total_train_step = total_train_step + 1if total_train_step % 100 == 0:print("训练次数：{}, Loss: {}".format(total_train_step, loss.item()))writer.add_scalar("train_loss", loss.item(), total_train_step)# 测试步骤开始tudui.eval()total_test_loss = 0total_accuracy = 0with torch.no_grad():for data in test_dataloader:imgs, targets = data~~imgs = imgs.to(device)targets = targets.to(device)~~ outputs = tudui(imgs)loss = loss_fn(outputs, targets)total_test_loss = total_test_loss + loss.item()accuracy = (outputs.argmax(1) == targets).sum()total_accuracy = total_accuracy + accuracyprint("整体测试集上的Loss: {}".format(total_test_loss))print("整体测试集上的正确率: {}".format(total_accuracy/test_data_size))writer.add_scalar("test_loss", total_test_loss, total_test_step)writer.add_scalar("test_accuracy", total_accuracy/test_data_size, total_test_step)total_test_step = total_test_step + 1torch.save(tudui, "tudui_{}.pth".format(i))print("模型已保存")writer.close()
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